Printers, copiers, and xerographic machines are machines that produce a pattern on a substrate. Patterns are the text and images of various colors, including black and white, and shades that can be printed on a substrate. Typically the machines consist of a means of obtaining the desired pattern and a marking engine that fixes the pattern to a substrate. Paper, cloth, and plastic are examples of substrates. A pattern can be fixed to a substrate using ink, pigments, or similar materials. Precise control is required to ensure that the pattern produced by the marking engine is acceptably similar to the desired pattern. One of the things that can cause incorrect patterning is substrate changes. A plastic substrate and a paper substrate can exhibit different patterns when subjected to the same marking process because the two substrates can accept ink differently or because the two substrates have different spectral characteristics.
Printing is a marking process in which a CMYK color space is often used in conjunction with cyan, magenta, yellow, and black inks. A pigment or other material can be used instead of ink. For simplicity, the term “ink” will be used here with the understanding that other equivalent materials can also be used to pattern a substrate. Specific colors can be achieved by dispensing precise amounts of each color of ink. For example, a mixture of half cyan ink and half magenta ink will produce a blue swatch in an ideal situation. Using more ink makes a more saturated color. However, the ideal is rarely obtained because inks are not perfect and different inks react differently with different substrates. To produce a specific blue color on a certain substrate, many different combinations of ink are tested until the right combination is found. To produce a palette of colors, many ink combinations are tested and the results stored in special tables called color tables. When a certain color is desired, the color table is consulted to find how much of each ink is required to make the color. Much of the art of color printing involves producing color tables.
Once a color table is determined, it can be used as a marking parameter. Marking parameters are used by a marking engine in the marking process in order to produce a desired pattern or color on a substrate. The problem is that most marking parameters are applicable only for certain substrates. An ink combination that produces one shade of blue on one substrate can produce a different shade of blue, or entirely a different color on a second substrate. Different color tables are therefore needed for the two substrates. The reason is that the two substrates exhibit different light reflection, color mixing and other physical properties. In general different marking parameters are required to produce the same pattern or color on different substrate types.
Typically, appropriate selection of marking parameters according to substrate requires human intervention. It takes considerable experience, expertise and skill to properly select marking parameters. Furthermore, it is possible that a change in substrate is not even noticed. In this case, the marking parameters are not appropriately updated. The results of failure to change or improperly changing marking parameters are inaccurate color output, unhappy customers, and increased costs. It is therefore advantageous to have an intelligent marking system that can automatically update the marking parameters according to the actual substrate.
One of the reasons different substrates can have different responses to the marking process is that they can have different spectral characteristics. Spectral characteristics refer to the manner in which an object responds to different wavelengths of incident light. In the case of a printable substrate, it refers to how the substrate reflects, absorbs, and scatters light at different wavelengths within the visible spectrum. A glossy white substrate and a matte textured substrate can exhibit quite different spectral characteristics. To quantify a substrate's spectral characteristics, a spectrophotometer can be used to measure its reflectance spectrum. One such device is an in-line spectrophotometer disclosed in U.S. Pat. No. 6,384,918, included by reference. Reflectance spectrums are often represented and treated as measurement vectors, such as w=|w(λ1)w(λ2) . . . w(λM)|T, where w(λ1) is the intensity of reflected light at a first wavelength, w(λ2) is the intensity of reflected light at a second wavelength and w(λM) is the intensity of reflected light at an Mth wavelength, and M is the number of wavelengths at which measurements are made. The superscript T indicates the matrix transpose operation.
By itself, a measurement has no meaning. It is only useful when interpreted within a meaningful context. There are many ways to interpret a measurement such as a reflectance spectrum produced by a spectrophotometer. One interpretation that is useful for the current problem of substrate identification is to classify the substrate reflectance measurement. A classifier, or classification algorithm, is an algorithm that takes target classification data corresponding to an unknown sample and assigns a category to which the unknown sample belongs. A category represents a collection of entities that share one or more common characteristics. For example, a fruit classifier could be designed to classify all fruit into the categories of apples, oranges, and bananas. When given an unknown fruit, such as a pear, the classifier would attempt to classify it among one of the known categories of apple, orange, or banana. An advanced fruit classifier might detect that the pear was in an unknown category. The reflectance spectrum of a substrate can be used as target classification data. Each category would represent a collection of substrates with substantially similar reflectance spectra.
Classifiers often use a distance calculation or correlation calculation to make a classification decision. Two common distance measures are the Euclidean distance and the Mahalinobis distance. Euclidean distance is calculated as √{square root over ((w1−w2)T(w1−w2))}{square root over ((w1−w2)T(w1−w2))} where w1 and w2 are vectors, as discussed above. Mahalinobis distance is often calculated as (w1−w2)TA(w1−w2), where A, a weighting matrix is usually the inverse of the measurement covariance matrix. A common correlation measure is w1Tw2 although the normalized version w1Tw2/(√{square root over (wiTwi)}√{square root over (w2Tw2)}) is also used. There are many other distance measures and correlation measures known to those skilled in the arts of pattern recognition, artificial intelligence, or signal processing.
Aspects of the embodiments overcome limitations and flaws of the prior art.